CHAOTIC ADVECTION IN A 2-D MIXED CONVECTION FLOW

Two-dimensional numerical simulations of particle advection in a channel flow with spatially periodic heating have been carried out. The velocity field is found to be periodic above a critical Rayleigh number of around 18 000 and a Reynolds number of 10. Particle motion becomes chaotic in the lower half plane almost immediately after this critical value is surpassed, as characterized by the power spectral density and Poincare section of the flow. As the Rayleigh number is increased further, particle motion in the entire domain becomes chaotic. (c) 1995 American Institute of Physics

Maximum-Entropy Inference with a Programmable Annealer

Optimisation problems in science and engineering typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this approach maximises the likelihood that the solution found is correct. An alternative approach is to make use of prior statistical information about the noise in conjunction with Bayes's theorem. The maximum entropy solution to the problem then takes the form of a Boltzmann distribution over the ground and excited states of the cost function. Here we use a programmable Josephson junction array for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that maximum entropy decoding at finite temperature can in certain cases give competitive and even slightly better bit-error-rates than the maximum likelihood approach at zero temperature, confirming that useful information can be extracted from the excited states of the annealing device. Furthermore we introduce a microscopic bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealing device samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a wide variety of machine learning applications which exploit maximum entropy inference, including natural language processing and image recognition. We further show that the limiting factor for performance in our experiments is likely to be control errors rather than failure to reach equilibrium. Our work also provides a method for determining if a system is in equilibrium which can be easily generalized. We discuss possible applications of this method to spin glasses and probing the performance of the quantum annealing algorithm.Comment: 9 figures in main text 9 figures in supplemental material. Significant amount of new Monte Carlo data added in v2 at referees request. Accepted for Scientific Report